如何按列和按行将我的数组标准化为 0 和 1

Question

我有一个数组，需要以结果为 0 到 1 之间的数字的方式对其进行规范化。我已经对整个数组进行了规范化，如下所示：

C = A / A.max(axis=0)

print(C)
____________________________________________________________________
[[0.         0.05263158 0.1        0.14285714 0.18181818 0.2173913 ]
 [0.33333333 0.36842105 0.4        0.42857143 0.45454545 0.47826087]
 [0.66666667 0.68421053 0.7        0.71428571 0.72727273 0.73913043]
 [1.         1.         1.         1.         1.         1.        ]]

但现在我必须按列和按行进行标准化。我怎样才能通过轴减少来做到这一点？如果有比我做的更好的方法，建议我修改。

我的预期结果是两个数组，其值已标准化。一个考虑列，另一个考虑行

这是我的数据

A = [[ 0  1  2  3  4  5]
 [ 6  7  8  9 10 11]
 [12 13 14 15 16 17]
 [18 19 20 21 22 23]]

Answer 1

My expected result is two arrays with the values normalized. One considering the columns and the other by the lines

a = np.array([[ 0,  1,  2,  3,  4,  5],
              [ 6,  7,  8,  9, 10, 11],
              [12, 13, 14, 15, 16, 17],
              [18, 19, 20, 21, 22, 23]])

如果

c = a / a.max(axis=0)

然后给你你想要的列

d = a / a.max(axis=1)[:,None]

行就够了。

>>> d.round(4)
array([[0.    , 0.2   , 0.4   , 0.6   , 0.8   , 1.    ],
       [0.5455, 0.6364, 0.7273, 0.8182, 0.9091, 1.    ],
       [0.7059, 0.7647, 0.8235, 0.8824, 0.9412, 1.    ],
       [0.7826, 0.8261, 0.8696, 0.913 , 0.9565, 1.    ]])

https://numpy.org/doc/stable/user/basics.broadcasting.html

Answer 2

你跳过了最小部分。通常 0-1 归一化要求从分母和分子中减去最小值。 https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range

import numpy as np

A = np.matrix([[ 0,  1,  2,  3,  4,  5],
 [ 6,  7,  8,  9, 10, 11],
 [12, 13, 14, 15, 16, 17],
 [18, 19, 20, 21, 22, 23]])

(A-A.min(axis=1))/(A.max(axis=1)-A.min(axis=1))
(A-A.min(axis=0))/(A.max(axis=0)-A.min(axis=0))

如何按列和按行将我的数组标准化为 0 和 1

How to normalize my array between 0 and 1 by column and by line

python

arrays

normalization

matrix